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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteYes, ocean–atmosphere patterns may provide seasonal clues to extreme heat in the Amazon—but the strongest reported signal is not the same as a reliable seven-month forecast. A 2026 study in Earth’s Future found that El Niño and tropical Atlantic conditions were statistically associated with Amazon hot extremes, with the broadest lagged temperature relationship appearing in March–May (MAM) at lags of up to seven months. The study evaluated forecasts of compound hot-and-dry events separately, and their skill varied by season and region.
What the study found—and what “early warning” means
Hobeichi and colleagues examined whether large-scale climate patterns precede extreme temperatures and rainfall conditions across the Amazon. They found positive-tail statistical dependence between positive phases of ENSO and tropical Atlantic variability and hot extremes. This is an association across observations, not proof that an ocean pattern causes a particular heat event or a warning for a specific location and date. The study in Earth’s Future describes the temperature relationships; it assesses compound-event prediction separately.
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The distinction matters because a lagged relationship can be detectable without being accurate enough to predict an event. “Up to seven months” refers to the lag of climate-index anomalies associated with MAM hot extremes. It does not mean the study demonstrated reliable forecasts of combined heat and rainfall deficits seven months ahead.
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| Season | Temperature relationships | Compound hot-dry prediction |
|---|---|---|
| December–February (DJF) | Recent or concurrent Tropical South Atlantic (TSA) warming is associated with hot extremes across the basin. Longer-lag Niño3.4 relationships appear in some southern and Andes–Amazon foothill areas. | Skill peaks in northern regions, according to the study. |
| March–May (MAM) | Lagged Niño3.4, Tropical North Atlantic (TNA), and TSA signals have widespread positive-tail dependence with hot extremes, with relationships extending to lags of up to seven months. | Skill peaks in the lower central Amazon. |
| June–August (JJA) | The study does not identify a comparably broad, long-lag temperature signal in its summary of seasonal findings. | Prediction skill is generally weakest. |
| September–November (SON) | Hot extremes are associated mainly with preceding TNA warming; lag patterns differ among climatic subregions. | Skill varies by region; the study’s summary does not identify a basin-wide peak for this season. |
The table separates observed temperature dependence from modelled compound-event skill; neither column should be read as a guarantee of a future event. The paper’s compound-event analysis reports that useful predictions generally required at least one climate index at a one-month lead. A particularly high regional skill estimate was based on very few events and therefore warrants caution.
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Why temperature signals are clearer than rainfall signals
Across the basin, the climate indices examined were more strongly and consistently related to temperature extremes than to precipitation extremes. Dependence involving three-month Standardized Precipitation Index (SPI-3) was generally weak and spatially sparse. That difference helps explain why evidence of a heat precursor should not be treated as equivalent evidence of a predictable drought or compound hot-dry event. The temperature and rainfall findings are distinct from the Random Forest tests of compound events.
Which climate patterns are involved
El Niño and Niño3.4
The study used Niño3.4 to represent ENSO. A plausible physical pathway described by the authors is that El Niño can weaken the Walker circulation and encourage subsidence over northern South America. Less rising air can mean less convection and cloud cover; reduced cloudiness can allow more incoming shortwave radiation, contributing to warmer surfaces and soil-moisture depletion. El Niño can also influence tropical Atlantic temperatures after its peak. These mechanisms offer context for the observed associations, but do not convert statistical dependence into a deterministic forecast.
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Tropical Atlantic variability
The analysis included Tropical North Atlantic (TNA) and Tropical South Atlantic (TSA) indices. TNA warming is associated with SON hot extremes, while recent or concurrent TSA warming is linked to DJF heat across the basin. The authors describe a possible rainfall pathway in which TNA warming helps keep the Atlantic Intertropical Convergence Zone farther north, suppressing rainfall over parts of the Amazon and northeastern Brazil. The seasonal and geographic differences mean neither index is a universal warning signal on its own.
North Atlantic Oscillation
The study also considered the North Atlantic Oscillation (NAO). Although direct tail dependence was weak, NAO appeared in some compound-event model settings. This illustrates why a climate index’s usefulness in a multivariable prediction model is not identical to its direct statistical relationship with an individual extreme.
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How the researchers assessed the patterns
The analysis used monthly CRU TS v4.08 gridded temperature and precipitation estimates at 0.5° by 0.5° resolution, derived from station observations. Copula models assessed tail dependence between the climate indices and extremes at monthly lags. Separate Random Forest experiments assessed the predictive usefulness of lagged indices for compound hot-dry extremes. The paper’s data and methods therefore cover both observed dependence and a distinct model-based prediction test, rather than a deployed public warning service.
The precipitation data have an important regional limitation: CRU may underestimate rainfall on the eastern Andean slopes because station coverage is sparse and the topography is difficult. That caveat is especially relevant when interpreting precipitation-related results for those areas.
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What this means for Amazon heat warnings
The findings support further work on seasonal risk assessment and climate services: climate patterns may help identify periods and regions with elevated heat risk. They do not establish that an operational warning system has been deployed, independently validated, or can provide reliable seven-month forecasts of compound heat and dryness. The study’s own results point to substantial variation by season and region, and sparse event counts limit confidence in some high skill estimates.
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